Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

Banner header
69.147 UPC E-Prints
You are here:
View Item 
  •   DSpace Home
  • E-prints
  • Grups de recerca
  • inSSIDE - integrated Software, Service, Information and Data Engineering
  • Articles de revista
  • View Item
  •   DSpace Home
  • E-prints
  • Grups de recerca
  • inSSIDE - integrated Software, Service, Information and Data Engineering
  • Articles de revista
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Mapreduce performance model for Hadoop 2.x

Thumbnail
View/Open
paper_is.pdf (919,5Kb)
 
10.1016/j.is.2017.11.006
 
  View UPCommons Usage Statistics
  LA Referencia / Recolecta stats
Includes usage data since 2022
Cita com:
hdl:2117/124328

Show full item record
Glushkova, DariaMés informació
Jovanovic, PetarMés informacióMés informacióMés informació
Abelló Gamazo, AlbertoMés informacióMés informacióMés informació
Document typeArticle
Defense date2019-01
PublisherElsevier
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
This work is protected by the corresponding intellectual and industrial property rights. Except where otherwise noted, its contents are licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that may provide reasonably accurate job response time estimation at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance model for Hadoop 2.x. While there are several efficient approaches for modeling the performance of MapReduce workloads in Hadoop 1.x, they could not be applied to Hadoop 2.x due to fundamental architectural changes and dynamic resource allocation in Hadoop 2.x. Thus, the proposed solution is based on an existing performance model for Hadoop 1.x, but taking into consideration architectural changes and capturing the execution flow of a MapReduce job by using queuing network model. This way, the cost model reflects the intra-job synchronization constraints that occur due the contention at shared resources. The accuracy of our solution is validated via comparison of our model estimates against measurements in a real Hadoop 2.x setup.
CitationGlushkova, D., Jovanovic, P., Abelló, A. Mapreduce performance model for Hadoop 2.x. "Information systems", Gener 2019, vol. 79, p. 32-43. 
URIhttp://hdl.handle.net/2117/124328
DOI10.1016/j.is.2017.11.006
ISSN0306-4379
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S0306437917304659
Collections
  • inSSIDE - integrated Software, Service, Information and Data Engineering - Articles de revista [113]
  • Departament d'Enginyeria de Serveis i Sistemes d'Informació - Articles de revista [246]
  • GESSI - Grup d'Enginyeria del Software i dels Serveis - Articles de revista [56]
  View UPCommons Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
paper_is.pdf919,5KbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Metadata under:Metadata under CC0
  • Contact Us
  • Send Feedback
  • Privacy Settings
  • Inici de la pàgina